Abstract Technology-enabled services (TESs), which use web-based and mobile applications supported by low- intensity coaching or care-management, have shown great potential, with a large number of randomized controlled trials consistently demonstrating efficacy. However, the many attempts to implement these validated interventions into large value-based care systems have failed. There are two broad reasons for these failures. First, patients in real world settings simply do not use the tools that were developed in research settings. Second, TESs have not been designed to fit into the workflows in general medicine practices. This research project will use a comprehensive user centered design approach to engage patients, care managers (CMs) and physicians in the design of a TES?comprised of technologies, CM service protocol, and implementation plan?that can be successfully deployed in a collaborative care program in family medicine clinics. The overall TES will be designed to support the existing collaborative care model, facilitating the acquisition of ongoing depression assessments from patients and communication with prescribing physicians. The design innovation focus of this research project will be to design a patient app that is simple, usable, useful, and fits into the fabric of people?s lives. We will harness ongoing research efforts in our Center in personal sensing, which use passively collected data from mobile phones to identify behaviors relevant to depression in real time. To date, we have created algorithms that reliably identify GPS mobility patterns, physical activity, locations visited, sleep patterns, and in-phone communication patterns, all of which track behaviors related to depression. This project will design patient interfaces that can represent this sensed information to patients in ways that are easily understandable, and that nudge people to increase positive activities, decrease depressogenic behaviors, and explore the relationship between behaviors and mood. We will use a behavior activation framework for the design of the intervention protocol. The TES will also include a CM dashboard that provides visibility into patient app use, as well as communication tools that allow the CM to provide low intensity support to the patient and communicate with the physician around pharmacotherapy needs. The effectiveness and implementation of the TES will be evaluated in a roll out cluster randomized trial across 4 primary care clinics This project will be the first to integrate the emerging capabilities of personal sensing into intervention apps. The resulting TES has the potential to be the first that is usable by real world patients, fits into clinic workflows, and can be successfully implemented in a general medicine collaborative care program.